Open Access

ARTICLE

Optimization of Operating Parameters for Underground Gas Storage Based on Genetic Algorithm

Yuming Luo¹, Wei Zhang², Anqi Zhao², Ling Gou¹, Li Chen¹, Yaling Yang¹, Xiaoping Wang¹, Shichang Liu¹, Huiqing Qi³, Shilai Hu^2,*

1 Chongqing Gas District, PetroChina Southwest Oil & Gasfield Company, Chongqing, 400021, China
2 School of Petroleum Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China
3 School of Electronic and Electrical Engineering, Nanyang Technological University, Singapore, 639798, Singapore

* Corresponding Author: Shilai Hu. Email:

(This article belongs to the Special Issue: Integrated Geology-Engineering Simulation and Optimizationfor Unconventional Oil and Gas Reservoirs)

Energy Engineering 2025, 122(8), 3201-3221. https://doi.org/10.32604/ee.2025.066507

Received 10 April 2025; Accepted 30 May 2025; Issue published 24 July 2025

Abstract

This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety. Previous research primarily focused on integrating reservoir, wellbore, and surface facility constraints, often resulting in broad constraint ranges and slow model convergence. To solve this problem, the present study introduces additional constraints on maximum withdrawal rates by combining binomial deliverability equations with material balance equations for closed gas reservoirs, while considering extreme peak-shaving demands. This approach effectively narrows the constraint range. Subsequently, a collaborative optimization model with maximum gas production as the objective function is established, and the model employs a joint solution strategy combining genetic algorithms and numerical simulation techniques. Finally, this methodology was applied to optimize operational parameters for Gas Storage T. The results demonstrate: (1) The convergence of the model was achieved after 6 iterations, which significantly improved the convergence speed of the model; (2) The maximum working gas volume reached 11.605 × 10⁸ m³, which increased by 13.78% compared with the traditional optimization method; (3) This method greatly improves the operation safety and the ultimate peak load balancing capability. The research provides important technical support for the intelligent decision of injection and production parameters of gas storage and improving peak load balancing ability.

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Keywords

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